Post correction reliability maps directly onto adversarial robustness and AI control research priorities. Temperature invariant failure in safety-tuned models (variance under 1pp across T=0.0 to T=0.8) is consistent with training-level constraint suppression rather than stochastic sampling drift. This is a different failure mode from jailbreaks and prompt injection. It persists under normal deployment conditions with no adversarial input required. Happy to discuss methodology with anyone working on alignment training robustness.

Austin, thank you so much! I wasn't sure if this idea would connect well with people outside my research bubble, so seeing someone actually back it is, to be honest, incredibly motivating!! It means a lot, thanks truly :)

I gave your project a read, i dont see how it is similar

We're buying more Orexin, last dose was too low.

90-day execution plan

Days 1–30: finalize trace schema, integrate one framework, define failure taxonomy.

Days 31–60: build initial adversarial corpus, implement CML graph logic, run first baseline comparisons.

Days 61–90: expand benchmark, publish evaluation write-up, release reusable library + documentation.

Thanks so much for your kind words and your donation, @PipFoweraker :)

Update:

I have active related applications under evaluation with LTFF, Open Philanthropy / Coefficient-affiliated funding, and NLNet, and I am currently clarifying fiscal sponsorship structure for an external SFF application. Current focus is tightening benchmark scope, deliverables, and release plan for LTP + CML.

Update:

I have active related applications under evaluation with LTFF, Open Philanthropy / Coefficient-affiliated funding, and NLNet, and I am currently clarifying fiscal sponsorship structure for an external SFF application. Current focus is tightening benchmark scope, deliverables, and release plan for LTP + CML.

Update: We are aligning fiscal sponsorship for an external SFF application. This project is gaining multi-fund interest.

Thank you @aashkapatel! Fingers crossed it will get funded. Rich

This is a high-impact project. And the theory of change really resonates very well. Hope this project gets funded, goodluck, @Dr. Richard Armitage :)

An unusual number of people making contributions to the field now came into it through unusual means, and many of them express regret at not having found out about it earlier.

I think strategies like this are a novel experiment in improving the rate of exposing bright minds to AI safety memes at scale and can pose as an effective filtering mechanism to spark both curiosity and engagement.

I'm excited by both the founders and the idea and think it has a lot of potential to succeed.

What progress have you made since your last update?

Things have progressed nicely. We acquired and installed a high quality industrial mixer (imported from Dubai) to rapidly increase the production of therapeutic food to treat childhood malnutrition and we have placed large orders for vital ingredients (oils/fatty acids and vitamin-mineral supplements) that must be imported to meet production demands. We have an ongoing evaluation project in rural areas to document the effectiveness of this intervention, working with a PhD candidate from Mekelle University, and we have a dedicated and reliable staff working on production and distribution.

The troubles in Tigray continue to be severe. The healthcare system is near collapse due to obstructive policies from the Ethiopian Federal Government, recently documented on the BBC's "Inside Africa" program. Supplies of fuel to the region are tightly restricted and the cost of gasoline is now around $25 USD per gallon. Transportation is very difficult. Almost everyone is walking because nobody can afford motor vehicle travel. This means that ambulance services etc are almost non-existent.

The political climate remains difficult. There are serious tensions between Ethiopia and neighboring Eritrea, which could break out into open conflict. Still, everyone associated with this project is enthusiastic as the benefits are clear and demand for "Fewsi Food" (our name for our peanut-butter based ready-to-use therapeutic food) is high.

What are your next steps?

We continue to work with our logistical supply chain to get the ingredients that we need. This is a long-term project as the need will be here for a long time.

Is there anything others could help you with?

We are still in need of cash assistance to purchase imported ingredients. We will need to order another 12 metric tons of powdered milk within a few months--a major cash outlay--so any help we can get would be greatly appreciated. We are in this for the long haul, with many thanks to our supporters!

Recommended an additional $50k to Lyptus on same thesis as the below. I'm still curious about Lyptus absorbing more than this, but I only had another $50k in my Manifund pot! I've reached out to some funders about considering funding Lyptus further. I hope my comment below can serve as some evidence. For now, without more regrantor recommendation budget, I'm bowing out. Wishing Sean et al. the best of luck!!

I wanted to share an update on this project -- actually two updates. I wrote a post at the end of 2025 on my website about some of the 2025 developments that I never got around to mentioning. And just a couple of days ago I wrote another post highlighting some of the linking work I've done in 2026 so far.

Here's a brief summary of the most recent developments:

• I'm writing a paper on LambertLink

• I found 3 new solar system objects that were missed in Rubin's DP1 data

• I developed a new linker for 3-nighters across apparitions: 3DB-3DB (another ITF-ITF linking method)

• I bought some new computers (thanks to this grant)

MTCP benchmark isolates post-correction reliability: the capacity to

maintain constraints after explicit correction within the same conversation.

181,448 evaluations across 32 production models show systematic failure:

• Best: 88.7% (grok-3-mini, Grade B)

• Flagship regression: GPT-4o scores 16.2pp below GPT-4o-mini

• Control probe degradation: all models collapse to 10-57.5% on fresh probes

• Exception: DeepSeek-R1 (5pp drop, contamination-resistant)

Framework published OSF.IO/DXGK5 (CC-BY-NC 4.0):

• MTCP three-turn protocol across five vectors (NCA, SFC, IDL, CG, LANG)

• IGS theory distinguishing architectural vs. stochastic failures

• Sigma-Forensics producing EU AI Act-compliant audit reports

Temperature invariance in Claude models (0.8pp variance T=0.0 to T=0.8)

suggests architectural constraint suppression vs. sampling-driven failures

in LLaMA models (7.1pp variance).

Funding advances: probe diversity, peer review, demographic vector,

statistical rigor.

Platform: mtcp.live

Contact: admin@mtcp.live

Doom Debates brings the right kind of content and packages it in the best possible way. I love watching their videos. I really hope that this project reaches its funding goal. Goodluck, Liron :)

Thank you for your support, Dr. Jones :)

I have confidence in Sean based on his previous work, and I think locations with great talent (like Australia, India, Germany, etc) must be funded appropriately in order to create institutional capacity to nurture that talent. I really really hope that this project gets funded. All the best, Sean and #TeamLyptus :)

I am so excited to see @aashkapatel engaged in this important project. I had the pleasure of appearing on her insightful podcast "On Air With Aashka" She is an outstanding talent who cares deeply about bettering our society. Her technical savvy along with her heart for people is a true success formula. Simply stated Aashka is one to watch, follow, and support!

Post correction reliability maps directly onto adversarial robustness and AI control research priorities. Temperature invariant failure in safety-tuned models (variance under 1pp across T=0.0 to T=0.8) is consistent with training-level constraint suppression rather than stochastic sampling drift. This is a different failure mode from jailbreaks and prompt injection. It persists under normal deployment conditions with no adversarial input required. Happy to discuss methodology with anyone working on alignment training robustness.

Austin, thank you so much! I wasn't sure if this idea would connect well with people outside my research bubble, so seeing someone actually back it is, to be honest, incredibly motivating!! It means a lot, thanks truly :)

I gave your project a read, i dont see how it is similar

We're buying more Orexin, last dose was too low.

90-day execution plan

Days 1–30: finalize trace schema, integrate one framework, define failure taxonomy.

Days 31–60: build initial adversarial corpus, implement CML graph logic, run first baseline comparisons.

Days 61–90: expand benchmark, publish evaluation write-up, release reusable library + documentation.

Thanks so much for your kind words and your donation, @PipFoweraker :)

Update:

I have active related applications under evaluation with LTFF, Open Philanthropy / Coefficient-affiliated funding, and NLNet, and I am currently clarifying fiscal sponsorship structure for an external SFF application. Current focus is tightening benchmark scope, deliverables, and release plan for LTP + CML.

Update:

I have active related applications under evaluation with LTFF, Open Philanthropy / Coefficient-affiliated funding, and NLNet, and I am currently clarifying fiscal sponsorship structure for an external SFF application. Current focus is tightening benchmark scope, deliverables, and release plan for LTP + CML.

Update: We are aligning fiscal sponsorship for an external SFF application. This project is gaining multi-fund interest.

Thank you @aashkapatel! Fingers crossed it will get funded. Rich

This is a high-impact project. And the theory of change really resonates very well. Hope this project gets funded, goodluck, @Dr. Richard Armitage :)

An unusual number of people making contributions to the field now came into it through unusual means, and many of them express regret at not having found out about it earlier.

I think strategies like this are a novel experiment in improving the rate of exposing bright minds to AI safety memes at scale and can pose as an effective filtering mechanism to spark both curiosity and engagement.

I'm excited by both the founders and the idea and think it has a lot of potential to succeed.

What progress have you made since your last update?

Things have progressed nicely. We acquired and installed a high quality industrial mixer (imported from Dubai) to rapidly increase the production of therapeutic food to treat childhood malnutrition and we have placed large orders for vital ingredients (oils/fatty acids and vitamin-mineral supplements) that must be imported to meet production demands. We have an ongoing evaluation project in rural areas to document the effectiveness of this intervention, working with a PhD candidate from Mekelle University, and we have a dedicated and reliable staff working on production and distribution.

The troubles in Tigray continue to be severe. The healthcare system is near collapse due to obstructive policies from the Ethiopian Federal Government, recently documented on the BBC's "Inside Africa" program. Supplies of fuel to the region are tightly restricted and the cost of gasoline is now around $25 USD per gallon. Transportation is very difficult. Almost everyone is walking because nobody can afford motor vehicle travel. This means that ambulance services etc are almost non-existent.

The political climate remains difficult. There are serious tensions between Ethiopia and neighboring Eritrea, which could break out into open conflict. Still, everyone associated with this project is enthusiastic as the benefits are clear and demand for "Fewsi Food" (our name for our peanut-butter based ready-to-use therapeutic food) is high.

What are your next steps?

We continue to work with our logistical supply chain to get the ingredients that we need. This is a long-term project as the need will be here for a long time.

Is there anything others could help you with?

We are still in need of cash assistance to purchase imported ingredients. We will need to order another 12 metric tons of powdered milk within a few months--a major cash outlay--so any help we can get would be greatly appreciated. We are in this for the long haul, with many thanks to our supporters!

Recommended an additional $50k to Lyptus on same thesis as the below. I'm still curious about Lyptus absorbing more than this, but I only had another $50k in my Manifund pot! I've reached out to some funders about considering funding Lyptus further. I hope my comment below can serve as some evidence. For now, without more regrantor recommendation budget, I'm bowing out. Wishing Sean et al. the best of luck!!

I wanted to share an update on this project -- actually two updates. I wrote a post at the end of 2025 on my website about some of the 2025 developments that I never got around to mentioning. And just a couple of days ago I wrote another post highlighting some of the linking work I've done in 2026 so far.

Here's a brief summary of the most recent developments:

• I'm writing a paper on LambertLink

• I found 3 new solar system objects that were missed in Rubin's DP1 data

• I developed a new linker for 3-nighters across apparitions: 3DB-3DB (another ITF-ITF linking method)

• I bought some new computers (thanks to this grant)

MTCP benchmark isolates post-correction reliability: the capacity to

maintain constraints after explicit correction within the same conversation.

181,448 evaluations across 32 production models show systematic failure:

• Best: 88.7% (grok-3-mini, Grade B)

• Flagship regression: GPT-4o scores 16.2pp below GPT-4o-mini

• Control probe degradation: all models collapse to 10-57.5% on fresh probes

• Exception: DeepSeek-R1 (5pp drop, contamination-resistant)

Framework published OSF.IO/DXGK5 (CC-BY-NC 4.0):

• MTCP three-turn protocol across five vectors (NCA, SFC, IDL, CG, LANG)

• IGS theory distinguishing architectural vs. stochastic failures

• Sigma-Forensics producing EU AI Act-compliant audit reports

Temperature invariance in Claude models (0.8pp variance T=0.0 to T=0.8)

suggests architectural constraint suppression vs. sampling-driven failures

in LLaMA models (7.1pp variance).

Funding advances: probe diversity, peer review, demographic vector,

statistical rigor.

Platform: mtcp.live

Contact: admin@mtcp.live

Doom Debates brings the right kind of content and packages it in the best possible way. I love watching their videos. I really hope that this project reaches its funding goal. Goodluck, Liron :)

Thank you for your support, Dr. Jones :)

I have confidence in Sean based on his previous work, and I think locations with great talent (like Australia, India, Germany, etc) must be funded appropriately in order to create institutional capacity to nurture that talent. I really really hope that this project gets funded. All the best, Sean and #TeamLyptus :)

I am so excited to see @aashkapatel engaged in this important project. I had the pleasure of appearing on her insightful podcast "On Air With Aashka" She is an outstanding talent who cares deeply about bettering our society. Her technical savvy along with her heart for people is a true success formula. Simply stated Aashka is one to watch, follow, and support!